Applied machine learning for smart data analysis

Bibliographische Detailangaben

Titel
Applied machine learning for smart data analysis
verantwortlich
Dey, Nilanjan (HerausgeberIn); Wagh, Sanjeev (HerausgeberIn); Mahalle, Parikshit N. (HerausgeberIn); Pathan, Mohd. Shafi (HerausgeberIn)
veröffentlicht
Boca Raton, London, New York: CRC Press, Taylor & Francis Group, [2019]
©2019
Erscheinungsjahr
2019
Teil von
Computational Intelligence in Engineering Problem Solving
Erscheint auch als
Applied machine learning for smart data analysis, Boca Raton : CRC Press, Taylor & Francis Group, 2019, xviii, 225 Seiten
Andere Ausgaben
Applied machine learning for smart data analysis
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Zusammenfassung
Section I. Machine learning -- Hindi and Urdu to English named entity statistical machine transliteration using source language word origin context -- Anti-depression psychotherapist Chatbot for exam and study-related stress -- Plagiasil : a plagiarism detector based on MAS scalable framework for research effort evaluation by unsupervised machine learning - hybrid plagiarism model -- Section II. Machine learning in data mining -- Digital image processing using wavelets : basic principles and application -- Probability predictor using data-mining techniques -- Big data summarization using modified fuzzy clustering algorithm, semantic feature, and data compression approach -- Topic-specific natural language Chatbot as General Advisor for college -- Section III. Machine learning in IoT -- Implementation of machine learning in the education sector : analyzing the causes behind average student guides -- Priority-based message-forwarding scheme in VANET with intelligent navigation -- Section IV. Machine learning in security -- A comparative analysis and discussion of email spam classification methods using machine learning techniques -- Malware prevention and detection system for smartphone : a machine learning approach.
Umfang
1 Online-Ressource; Illustrationen
Sprache
Englisch
Schlagworte
BK-Notation
54.72 Künstliche Intelligenz
ISBN
9780429440953
DOI
10.1201/9780429440953